Automatic computerized analysis of medical imaging data is becoming an increasingly important and acceptable method of working with vast amounts of data produced by modern medical diagnostic equipment. The reliability of the results produced by the automatic computerized analysis of the medical data is very important. The reliability and the robustness of automatic computerized analysis systems makes it either efficient or useless in a real medical world scenario.
No computer system can produce 100% correct results 100% of the time, which means that any automatic analysis system will fail eventually to analyze the data correctly. Such failures may be tolerable if the system automatically recognizes them and reports an inability to cope with the input to the operator. A failure report is more useful if the system provides additional details concerning the reason for the failure. For example, if the input data was of insufficient quality for automatic analysis, the input data is probably also of insufficient quality for a human to read as well. As a result, the medical test should be repeated.
In general, existing medical imaging data analysis tools do not provide a fully automatic solution and instead rely on the user's judgment concerning the quality of the result. As a result, these types of analysis tools assume a significant amount of user interaction, and thus, reserve the image understanding to the human operator. Another group of existing systems, which fall into the category of computer aided detection/diagnosis (CAD) tools, are usually more automatic and do include computerized image understanding, reasoning, and decision making. However, existing CAD systems lack a self-diagnosis capability, which could reduce the error rate by reporting cases where a reliable diagnosis is not possible and/or where the system is not confident in the validity of the result. Thus, a method and a system for an automatic self-diagnosis of medical CAD systems that allows automatic detection of cases in which automatic processing fails or produces unreliable results is needed. A method and a system for automatically quantifying an estimation of an image quality is also needed.